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CellSage is an advanced software platform for battery health modeling, simulation, and analysis, developed through extensive research at the U.S. Department of Energy's Idaho National Laboratory and Ridgetop Group. This tool provides a comprehensive suite of features that enable accurate predictions of lithium-ion battery aging under real-world conditions. It is designed for industries that require precise battery diagnostics, offering insights that support optimal battery management and enhance safety protocols. Featuring an intuitive GUI, CellSage allows users to visualize battery aging and life expectancy clearly. It includes an extensive library of pre-defined battery types with options for expansion, facilitating diagnostic and prognostic evaluations. The software's robust analysis capabilities span multiple environmental and operational parameters, offering customized modeling to suit various deployment conditions. Ideal for researchers and manufacturers, CellSage supports the reduction of R&D costs and aids in optimizing battery designs. It's well-suited for academic and industrial use, reinforcing battery management systems and providing reliable state-of-health assessments and life predictions. With collaborative opportunities for integration into wider systems, CellSage is at the forefront of battery health technology.
The Advanced Electrolyte Model (AEM) is a cutting-edge molecular simulation tool designed to optimize electrolyte chemistry. Developed by Idaho National Laboratory and distributed by Ridgetop Group, AEM serves as a virtual laboratory that facilitates the exploration and fine-tuning of electrolyte properties at the molecular level. The tool allows scientists to conduct experiments that generate over 100 property metrics per simulation. AEM's comprehensive database includes more than 50 solvents and 30 salts, enabling its vast application in battery research. Grounded in Nonprimitive, Nonrestricted Associated form of the Mean Spherical Approximation (NPNRAMSA) and coupled with an ion-solvation equation of state, AEM offers highly accurate predictions with deviations often below 5-10%. It is used worldwide to accelerate the shift from fossil fuels towards electric vehicles and grid-scale battery systems. The model not only enhances battery research but also supports larger energy conservation goals, facilitating cost-effective energy storage solutions. AEM provides battery researchers and manufacturers with a versatile toolkit to revolutionize energy application, making it a cornerstone in the sustainable energy movement.
Sentinel Motion is a comprehensive IoT-based sensor system developed for monitoring critical equipment through a combination of temperature, linear, rotary, or vibrational force measurements. Originally designed for helicopter gearbox systems, this technology adapts to various industrial applications including railways. It combines the capabilities of traditional detection equipment into a cost-effective, continuous monitoring and alert system. The system includes a network of wireless RotoSense smart sensors, the Sentinel Gateway communications device, and the Sentinel MotionView software, enabling real-time data analysis and sensor management. This toolbox allows for precise monitoring of tracks, wheels, and bearings, offering railroad operators an effective solution to reduce operational costs from equipment failures. Sentinel Motion's testing has demonstrated its viability for fault detection and infrastructure characterization in the rail industry. By integrating advanced sensor data analytics with IoT communication tools, Sentinel Motion ensures proactive maintenance and greater reliability for rail systems, exemplifying Ridgetop's commitment to innovative asset management solutions.
The Adaptive Remaining Useful Life Estimator (ARULE) is a sophisticated predictive analytics tool designed to forecast key indicators such as Remaining Useful Life (RUL), State of Health (SoH), and Prognostic Horizon (PH) in complex systems. ARULE is versatile, handling electrical, mechanical, and electro-mechanical fatigue damage through its advanced condition-based monitoring and data analysis techniques. Utilizing a proprietary approach involving Extended Kalman Filtering, the system processes condition-based feature data to offer early warnings on potential failures. ARULE's interactive graphical user interface (GUI) allows users to upload and process data streams for target systems, enhancing the overall maintenance and health management strategies. The software platform supports defining user-specific parameters for efficient data analysis, delivering prognostic estimates that guide the maintenance of systems based on actual conditions rather than arbitrary timelines. This tool is an integral part of Ridgetop's Sentinel Suite, bolstering the predictive capabilities of Sentinel Power, Motion, and IT modules. By applying ARULE, industries can maintain operational systems more effectively, reducing downtime and optimizing performance across applications like power supply systems, battery management, and industrial automation.
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